Hierarchical heterogeneous particle swarm optimization

Xinpei Ma, Hiroki Sayama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Particle swarm optimization (PSO) has recently been modified to several versions. Heterogeneous PSO is a recent extension which includes behavioral heterogeneity of particles. Here we propose a further developed version that has hierarchical interaction patterns among heterogeneous particles, which we call hierarchical heterogeneous PSO (HHPSO). Two algorithm designs that have been developed and tested are multi-layer HHPSO (ml-HHPSO) and multi-group HHPSO (mg-HHPSO). The performances of these algorithms were measured on a set of benchmark functions and compared with standard PSO and heterogeneous PSO. The results showed that the performances of both HHPSO algorithms were significantly improved from standard PSO and heterogeneous PSO, with higher quality of optimal solutions and faster convergence speed.

Original languageEnglish
Title of host publicationArtificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
EditorsHiroki Sayama, John Rieffel, Sebastian Risi, Rene Doursat, Hod Lipson
PublisherMIT Press Journals
Pages629-630
Number of pages2
ISBN (Electronic)9780262326216
Publication statusPublished - 2014
Externally publishedYes
Event14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 - Manhattan, United States
Duration: 2014 Jul 302014 Aug 2

Publication series

NameArtificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014

Conference

Conference14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014
CountryUnited States
CityManhattan
Period14/7/3014/8/2

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence
  • Modelling and Simulation

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